Mahotas – Conditional Watershed of Image
In this article, we will see how we can do a conditional watershed of the image in mahotas. In the study of image processing, a watershed is a transformation defined on a grayscale image. The name refers metaphorically to a geological watershed, or drainage divide, which separates adjacent drainage basins.
In this tutorial, we will use the “Lena” image, below is the command to load it.
mahotas.demos.load('lena')
Below is the lena image
In order to do this we will use mahotas.cwatershed method
Syntax : mahotas.cwatershed(img, marker)
Argument : It takes image object and labeled marker as argument
Return : It returns image object
Note: Input image should be filtered or should be loaded as grey
In order to filter the image we will take the image object which is numpy.ndarray and filter it with the help of indexing, below is the command to do this
image = image[:, :, 0]
Below is the implementation
Python3
# importing required libraries import mahotas import mahotas.demos from pylab import gray, imshow, show import numpy as np # loading image img = mahotas.demos.load( 'lena' ) # filtering image img = img. max ( 2 ) # otsu method T_otsu = mahotas.otsu(img) # image values should be greater than otsu value img = img > T_otsu print ( "Image threshold using Otsu Method" ) # creating a labelled image marker, n_nucleus = mahotas.label(img) # showing image imshow(img) show() # watershed of image new_img = mahotas.cwatershed(img, marker) print ( "CWatershed Image" ) # showing image imshow(new_img) show() |
Output :
Image threshold using Otsu Method
CWatershed Image
Another example
Python3
# importing required libraries import mahotas import numpy as np from pylab import gray, imshow, show import os # loading image img = mahotas.imread( 'dog_image.png' ) # filtering image img = img[:, :, 0 ] # otsu method T_otsu = mahotas.otsu(img) # image values should be greater than otsu value img = img > T_otsu print ( "Image threshold using Otsu Method" ) # showing image imshow(img) show() # creating a labelled image marker, n_nucleus = mahotas.label(img) # watershed of image new_img = mahotas.cwatershed(img, marker) print ( "CWatershed Image" ) # showing image imshow(new_img) show() |
Output:
Image threshold using Otsu Method
CWatershed Image
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